# -*- coding:utf-8 -*-
# http://yongyuan.name/blog/pedestrian-detection-opencv.html
# import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2

# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

cameraCapture = cv2.VideoCapture(0)
success, image = cameraCapture.read()
# loop over the image paths
while success and cv2.waitKey(1) == -1:
    success, image = cameraCapture.read()
    image = imutils.resize(image, width=min(400, image.shape[1]))
    orig = image.copy()

	# detect people in the image
    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)

	# draw the original bounding boxes
    for (x, y, w, h) in rects:
		cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)

	# apply non-maxima suppression to the bounding boxes using a
	# fairly large overlap threshold to try to maintain overlapping
	# boxes that are still people
    rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
    pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)

	# draw the final bounding boxes
    for (xA, yA, xB, yB) in pick:
		cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)

	# show some information on the number of bounding boxes
	#filename = imagePath[imagePath.rfind("/") + 1:]
	#print("[INFO] {}: {} original boxes, {} after suppression".format(filename, len(rects), len(pick)))

	# show the output images
    cv2.imshow("Before NMS", orig)
    cv2.imshow("After NMS", image)
    cv2.waitKey(0)